Day53 -Introduction_of_Seaborn.ipynb

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"
        }
      },
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![image.png](attachment:image.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ozKoZMhbEtW"
      },
      "source": [
        "Seaborn is a data visualization library in Python that is based on Matplotlib. It provides a high-level interface for creating informative and attractive statistical graphics. Seaborn comes with several built-in themes and color palettes to make it easy to create aesthetically pleasing visualizations.\n",
        "\n",
        "Documentation link : https://seaborn.pydata.org/\n",
        "\n",
        "Some key features of Seaborn include:\n",
        "\n",
        "1. **Statistical Plots:** Seaborn simplifies the process of creating statistical plots by providing functions for common statistical visualizations such as scatter plots, line plots, bar plots, box plots, violin plots, and more.\n",
        "\n",
        "2. **Themes and Color Palettes:** Seaborn allows you to easily customize the look of your visualizations by providing different themes and color palettes. This makes it simple to create professional-looking plots without having to manually tweak every detail.\n",
        "\n",
        "3. **Categorical Plots:** Seaborn excels at visualizing relationships in categorical data. It has functions specifically designed for working with categorical variables, making it easy to create plots that show the distribution of data across different categories.\n",
        "\n",
        "4. **Matrix Plots:** Seaborn provides functions for visualizing matrices of data, such as heatmaps. These can be useful for exploring relationships and patterns in large datasets.\n",
        "\n",
        "5. **Time Series Plots:** Seaborn supports the visualization of time series data, allowing you to create informative plots for temporal analysis.\n",
        "\n",
        "While Seaborn is built on top of Matplotlib, it abstracts away much of the complexity and provides a more concise and visually appealing syntax for creating statistical visualizations. It is a popular choice among data scientists and analysts for quickly generating exploratory data visualizations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7otKIp59bEtX"
      },
      "source": [
        "Seaborn offers several other plot types for various visualization needs:\n",
        "\n",
        "1. **Categorical Plots:**\n",
        "   - **`barplot`**: Displays the central tendency and confidence interval of a numeric variable for different levels of a categorical variable.\n",
        "\n",
        "   - **`countplot`**: Shows the count of observations in each category of a categorical variable using bars.\n",
        "\n",
        "   - **`boxplot`**: Illustrates the distribution of a continuous variable across different categories, emphasizing quartiles and potential outliers.\n",
        "\n",
        "   - **`violinplot`**: Combines aspects of a box plot and a kernel density plot, providing insights into the distribution of a variable for different categories.\n",
        "\n",
        "   - **`stripplot` and `swarmplot`**: Visualize individual data points along with a categorical distribution. `stripplot` places points on a single axis, while `swarmplot` adjusts the points to avoid overlap.\n",
        "\n",
        "2. **Distribution Plots:**\n",
        "   - **`histplot`**: Creates a histogram to display the distribution of a single variable.\n",
        "\n",
        "   - **`kdeplot`**: Plots the Kernel Density Estimation of a univariate or bivariate dataset, providing a smoothed representation of the distribution.\n",
        "\n",
        "   - **`rugplot`**: Adds small vertical lines (rug) to a plot to indicate the distribution of data points along the x or y-axis.\n",
        "\n",
        "3. **Regression Plots:**\n",
        "   - **`lmplot`**: Combines regression plots with FacetGrid to allow visualization of relationships between variables across multiple subplots.\n",
        "\n",
        "   - **`regplot`**: Creates a scatter plot with a linear regression line fitted to the data.\n",
        "\n",
        "4. **Time Series Plots:**\n",
        "   - **`tsplot`**: Formerly used for time series data, it has been replaced by the more flexible `lineplot`. However, it's still available for backward compatibility.\n",
        "\n",
        "5. **Facet Grids:**\n",
        "   - **`FacetGrid`**: Although not a specific plot type, `FacetGrid` is a powerful tool that allows you to create a grid of subplots based on the values of one or more categorical variables. It can be used with various plot types to create a matrix of visualizations.\n",
        "\n",
        "6. **Relational Plots:**\n",
        "   - **`relplot`**: Combines aspects of scatter plots and line plots to visualize the relationship between two variables across different levels of a third variable. It's a flexible function that can create scatter plots, line plots, or other types of relational plots.\n",
        "\n",
        "   - **`scatterplot`**: Creates a simple scatter plot to show the relationship between two variables.\n",
        "\n",
        "   - **`lineplot`**: Generates line plots to depict the trend between two variables.\n",
        "\n",
        "   - **`jointplot`**: Combines scatter plots for two variables along with histograms for each variable.\n",
        "\n",
        "7. **Matrix Plots:**\n",
        "   - **`heatmap`**: Displays a matrix where the values are represented by colors. Heatmaps are often used to visualize correlation matrices or other two-dimensional datasets.\n",
        "\n",
        "   - **`clustermap`**: Hierarchically clusters and orders rows and columns in a heatmap, which can be useful for exploring patterns in the data.\n",
        "\n",
        "   - **`pairplot`**: Creates a matrix of scatterplots for multiple pairs of variables. Diagonal elements show univariate distributions, while off-diagonal elements show bivariate relationships.\n",
        "\n",
        "   - **`jointplot` with `kind='hex'` or `kind='kde'`**: While commonly used for scatter plots, `jointplot` can display hexbin plots or kernel density estimation plots, providing a different perspective on the distribution of data points.\n",
        "\n",
        "   - **`corrplot`**: Visualizes a correlation matrix with colored cells, making it easy to identify strong and weak correlations between variables.\n",
        "\n",
        "   - **`catplot` with `kind='point'` or `kind='bar'`**: Allows you to create categorical plots with a matrix structure, where the rows and columns represent different categorical variables.\n",
        "\n",
        "These functions provide a range of tools for exploring relationships in data, whether you're interested in visualizing the distribution of individual variables, the relationship between two variables, or patterns within a matrix of data.\n",
        "\n",
        "These are just a selection of Seaborn's capabilities. The library is designed to make it easy to create a wide range of statistical graphics for data exploration and presentation. The choice of which plot to use depends on the nature of your data and the specific insights you want to extract."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g06l-J9vbEtY"
      },
      "source": [
        "In Seaborn, the concepts of \"axis-level\" and \"figure-level\" functions refer to the level at which the functions operate and the structure of the resulting plots.\n",
        "\n",
        "1. **Axis-Level Functions:**\n",
        "   - **Operate at the level of a single subplot or axis.**\n",
        "   - **Produce a single plot by default.**\n",
        "   - **Examples include functions like `sns.scatterplot()`, `sns.lineplot()`, `sns.boxplot()`, etc.**\n",
        "   - **Accept the `ax` parameter to specify the Axes where the plot will be drawn. If not specified, a new Axes is created.**\n",
        "\n",
        "   \n",
        "\n",
        "2. **Figure-Level Functions:**\n",
        "   - **Operate at the level of the entire figure, potentially creating multiple subplots.**\n",
        "   - **Produce a FacetGrid or a similar object that can be used to create a grid of subplots.**\n",
        "   - **Examples include functions like `sns.relplot()`, `sns.catplot()`, `sns.pairplot()`, etc.**\n",
        "   - **Return a FacetGrid object, allowing for easy creation of subplots based on additional categorical variables.**\n",
        "\n",
        "   \n",
        "\n",
        "The choice between axis-level and figure-level functions depends on your specific needs. Axis-level functions are often more straightforward for simple plots, while figure-level functions are powerful for creating complex visualizations with multiple subplots or facets based on categorical variables.\n",
        "\n",
        "Keep in mind that figure-level functions return objects like FacetGrid, and you can customize the resulting plots further using the methods and attributes of these objects. The documentation for each function provides details on how to use and customize the output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvHQ0IAKbEtY"
      },
      "source": [
        "## **1. Relational Plots**\n",
        "\n",
        "- to see the statistical relation between 2 or more variables.\n",
        "- Bivariate Analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "pfd4yBeWbEta"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "mT1vcwRnbEtb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "z6S7BjkSbEtb",
        "outputId": "7611b6b2-f4c4-4e8a-f7f5-5d810b880519"
      },
      "outputs": [
        {
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              "        document.querySelector('#df-1440263c-aeac-4838-8e24-386c66ba0b62 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     total_bill   tip     sex smoker   day    time  size\n",
              "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
              "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
              "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
              "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
              "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
              "..          ...   ...     ...    ...   ...     ...   ...\n",
              "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
              "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
              "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
              "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
              "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
              "\n",
              "[244 rows x 7 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tips = sns.load_dataset('tips')\n",
        "tips"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "hiYGw5RzbEtb",
        "outputId": "309a4c5f-dd23-4740-d0d3-863689cca657"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='total_bill', ylabel='tip'>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# scatter plot -> axes level function\n",
        "sns.scatterplot(data=tips, x='total_bill', y='tip',hue='sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "uGpIxA4kbqLM",
        "outputId": "866b3585-4969-4ce8-d1d7-b64439c30441"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f686180b340>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600.25x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# relplot -> figure level -> square shape\n",
        "sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter',hue='sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "6ZPDm9PubrsI",
        "outputId": "219c22f2-0ef7-4cd6-a598-b49c69e7dbb1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7e5ddfe6-00b6-4cd5-93f3-45489ed3f15d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>continent</th>\n",
              "      <th>year</th>\n",
              "      <th>lifeExp</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdpPercap</th>\n",
              "      <th>iso_alpha</th>\n",
              "      <th>iso_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1952</td>\n",
              "      <td>37.373</td>\n",
              "      <td>372000000</td>\n",
              "      <td>546.565749</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1957</td>\n",
              "      <td>40.249</td>\n",
              "      <td>409000000</td>\n",
              "      <td>590.061996</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>698</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1962</td>\n",
              "      <td>43.605</td>\n",
              "      <td>454000000</td>\n",
              "      <td>658.347151</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>699</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1967</td>\n",
              "      <td>47.193</td>\n",
              "      <td>506000000</td>\n",
              "      <td>700.770611</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>700</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1972</td>\n",
              "      <td>50.651</td>\n",
              "      <td>567000000</td>\n",
              "      <td>724.032527</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>701</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1977</td>\n",
              "      <td>54.208</td>\n",
              "      <td>634000000</td>\n",
              "      <td>813.337323</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>702</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1982</td>\n",
              "      <td>56.596</td>\n",
              "      <td>708000000</td>\n",
              "      <td>855.723538</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1987</td>\n",
              "      <td>58.553</td>\n",
              "      <td>788000000</td>\n",
              "      <td>976.512676</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1992</td>\n",
              "      <td>60.223</td>\n",
              "      <td>872000000</td>\n",
              "      <td>1164.406809</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1997</td>\n",
              "      <td>61.765</td>\n",
              "      <td>959000000</td>\n",
              "      <td>1458.817442</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2002</td>\n",
              "      <td>62.879</td>\n",
              "      <td>1034172547</td>\n",
              "      <td>1746.769454</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2007</td>\n",
              "      <td>64.698</td>\n",
              "      <td>1110396331</td>\n",
              "      <td>2452.210407</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e5ddfe6-00b6-4cd5-93f3-45489ed3f15d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7e5ddfe6-00b6-4cd5-93f3-45489ed3f15d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7e5ddfe6-00b6-4cd5-93f3-45489ed3f15d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-55eb6331-531a-4984-ac73-d589f81a29c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-55eb6331-531a-4984-ac73-d589f81a29c2')\"\n",
              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55eb6331-531a-4984-ac73-d589f81a29c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    country continent  year  lifeExp         pop    gdpPercap iso_alpha  \\\n",
              "696   India      Asia  1952   37.373   372000000   546.565749       IND   \n",
              "697   India      Asia  1957   40.249   409000000   590.061996       IND   \n",
              "698   India      Asia  1962   43.605   454000000   658.347151       IND   \n",
              "699   India      Asia  1967   47.193   506000000   700.770611       IND   \n",
              "700   India      Asia  1972   50.651   567000000   724.032527       IND   \n",
              "701   India      Asia  1977   54.208   634000000   813.337323       IND   \n",
              "702   India      Asia  1982   56.596   708000000   855.723538       IND   \n",
              "703   India      Asia  1987   58.553   788000000   976.512676       IND   \n",
              "704   India      Asia  1992   60.223   872000000  1164.406809       IND   \n",
              "705   India      Asia  1997   61.765   959000000  1458.817442       IND   \n",
              "706   India      Asia  2002   62.879  1034172547  1746.769454       IND   \n",
              "707   India      Asia  2007   64.698  1110396331  2452.210407       IND   \n",
              "\n",
              "     iso_num  \n",
              "696      356  \n",
              "697      356  \n",
              "698      356  \n",
              "699      356  \n",
              "700      356  \n",
              "701      356  \n",
              "702      356  \n",
              "703      356  \n",
              "704      356  \n",
              "705      356  \n",
              "706      356  \n",
              "707      356  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# line plot\n",
        "gap = px.data.gapminder()\n",
        "temp_df = gap[gap['country'] == 'India']\n",
        "temp_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "py3PA63Kbxzi",
        "outputId": "eb0245b4-86a1-4171-ceb5-fd413f1b15a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='year', ylabel='lifeExp'>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# axes level function\n",
        "sns.lineplot(data=temp_df, x='year', y='lifeExp')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "fmeZaRijbzxJ",
        "outputId": "91d134a6-878a-4768-ca33-34bb76d97240"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f686180bf70>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# using relpplot\n",
        "sns.relplot(data=temp_df, x='year', y='lifeExp', kind='line')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SoDKqCbLb1lJ",
        "outputId": "b20283d5-65d3-4ec0-9c95-b62728c1233a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f8fcc7d9-a025-414a-b855-fb30d37e3a94\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>continent</th>\n",
              "      <th>year</th>\n",
              "      <th>lifeExp</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdpPercap</th>\n",
              "      <th>iso_alpha</th>\n",
              "      <th>iso_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1952</td>\n",
              "      <td>50.917</td>\n",
              "      <td>56602560</td>\n",
              "      <td>2108.944355</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1957</td>\n",
              "      <td>53.285</td>\n",
              "      <td>65551171</td>\n",
              "      <td>2487.365989</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1962</td>\n",
              "      <td>55.665</td>\n",
              "      <td>76039390</td>\n",
              "      <td>3336.585802</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1967</td>\n",
              "      <td>57.632</td>\n",
              "      <td>88049823</td>\n",
              "      <td>3429.864357</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1972</td>\n",
              "      <td>59.504</td>\n",
              "      <td>100840058</td>\n",
              "      <td>4985.711467</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1977</td>\n",
              "      <td>61.489</td>\n",
              "      <td>114313951</td>\n",
              "      <td>6660.118654</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1982</td>\n",
              "      <td>63.336</td>\n",
              "      <td>128962939</td>\n",
              "      <td>7030.835878</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1987</td>\n",
              "      <td>65.205</td>\n",
              "      <td>142938076</td>\n",
              "      <td>7807.095818</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1992</td>\n",
              "      <td>67.057</td>\n",
              "      <td>155975974</td>\n",
              "      <td>6950.283021</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>1997</td>\n",
              "      <td>69.388</td>\n",
              "      <td>168546719</td>\n",
              "      <td>7957.980824</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>2002</td>\n",
              "      <td>71.006</td>\n",
              "      <td>179914212</td>\n",
              "      <td>8131.212843</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Americas</td>\n",
              "      <td>2007</td>\n",
              "      <td>72.390</td>\n",
              "      <td>190010647</td>\n",
              "      <td>9065.800825</td>\n",
              "      <td>BRA</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1952</td>\n",
              "      <td>67.500</td>\n",
              "      <td>69145952</td>\n",
              "      <td>7144.114393</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1957</td>\n",
              "      <td>69.100</td>\n",
              "      <td>71019069</td>\n",
              "      <td>10187.826650</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1962</td>\n",
              "      <td>70.300</td>\n",
              "      <td>73739117</td>\n",
              "      <td>12902.462910</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1967</td>\n",
              "      <td>70.800</td>\n",
              "      <td>76368453</td>\n",
              "      <td>14745.625610</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1972</td>\n",
              "      <td>71.000</td>\n",
              "      <td>78717088</td>\n",
              "      <td>18016.180270</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1977</td>\n",
              "      <td>72.500</td>\n",
              "      <td>78160773</td>\n",
              "      <td>20512.921230</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1982</td>\n",
              "      <td>73.800</td>\n",
              "      <td>78335266</td>\n",
              "      <td>22031.532740</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1987</td>\n",
              "      <td>74.847</td>\n",
              "      <td>77718298</td>\n",
              "      <td>24639.185660</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>572</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1992</td>\n",
              "      <td>76.070</td>\n",
              "      <td>80597764</td>\n",
              "      <td>26505.303170</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1997</td>\n",
              "      <td>77.340</td>\n",
              "      <td>82011073</td>\n",
              "      <td>27788.884160</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2002</td>\n",
              "      <td>78.670</td>\n",
              "      <td>82350671</td>\n",
              "      <td>30035.801980</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2007</td>\n",
              "      <td>79.406</td>\n",
              "      <td>82400996</td>\n",
              "      <td>32170.374420</td>\n",
              "      <td>DEU</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1952</td>\n",
              "      <td>37.373</td>\n",
              "      <td>372000000</td>\n",
              "      <td>546.565749</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1957</td>\n",
              "      <td>40.249</td>\n",
              "      <td>409000000</td>\n",
              "      <td>590.061996</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>698</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1962</td>\n",
              "      <td>43.605</td>\n",
              "      <td>454000000</td>\n",
              "      <td>658.347151</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>699</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1967</td>\n",
              "      <td>47.193</td>\n",
              "      <td>506000000</td>\n",
              "      <td>700.770611</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>700</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1972</td>\n",
              "      <td>50.651</td>\n",
              "      <td>567000000</td>\n",
              "      <td>724.032527</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>701</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1977</td>\n",
              "      <td>54.208</td>\n",
              "      <td>634000000</td>\n",
              "      <td>813.337323</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>702</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1982</td>\n",
              "      <td>56.596</td>\n",
              "      <td>708000000</td>\n",
              "      <td>855.723538</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1987</td>\n",
              "      <td>58.553</td>\n",
              "      <td>788000000</td>\n",
              "      <td>976.512676</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1992</td>\n",
              "      <td>60.223</td>\n",
              "      <td>872000000</td>\n",
              "      <td>1164.406809</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1997</td>\n",
              "      <td>61.765</td>\n",
              "      <td>959000000</td>\n",
              "      <td>1458.817442</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2002</td>\n",
              "      <td>62.879</td>\n",
              "      <td>1034172547</td>\n",
              "      <td>1746.769454</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2007</td>\n",
              "      <td>64.698</td>\n",
              "      <td>1110396331</td>\n",
              "      <td>2452.210407</td>\n",
              "      <td>IND</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8fcc7d9-a025-414a-b855-fb30d37e3a94')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-f8fcc7d9-a025-414a-b855-fb30d37e3a94 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-f8fcc7d9-a025-414a-b855-fb30d37e3a94');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31277b25-5e2e-4fd2-80b3-ec4fd6561dc0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     country continent  year  lifeExp         pop     gdpPercap iso_alpha  \\\n",
              "168   Brazil  Americas  1952   50.917    56602560   2108.944355       BRA   \n",
              "169   Brazil  Americas  1957   53.285    65551171   2487.365989       BRA   \n",
              "170   Brazil  Americas  1962   55.665    76039390   3336.585802       BRA   \n",
              "171   Brazil  Americas  1967   57.632    88049823   3429.864357       BRA   \n",
              "172   Brazil  Americas  1972   59.504   100840058   4985.711467       BRA   \n",
              "173   Brazil  Americas  1977   61.489   114313951   6660.118654       BRA   \n",
              "174   Brazil  Americas  1982   63.336   128962939   7030.835878       BRA   \n",
              "175   Brazil  Americas  1987   65.205   142938076   7807.095818       BRA   \n",
              "176   Brazil  Americas  1992   67.057   155975974   6950.283021       BRA   \n",
              "177   Brazil  Americas  1997   69.388   168546719   7957.980824       BRA   \n",
              "178   Brazil  Americas  2002   71.006   179914212   8131.212843       BRA   \n",
              "179   Brazil  Americas  2007   72.390   190010647   9065.800825       BRA   \n",
              "564  Germany    Europe  1952   67.500    69145952   7144.114393       DEU   \n",
              "565  Germany    Europe  1957   69.100    71019069  10187.826650       DEU   \n",
              "566  Germany    Europe  1962   70.300    73739117  12902.462910       DEU   \n",
              "567  Germany    Europe  1967   70.800    76368453  14745.625610       DEU   \n",
              "568  Germany    Europe  1972   71.000    78717088  18016.180270       DEU   \n",
              "569  Germany    Europe  1977   72.500    78160773  20512.921230       DEU   \n",
              "570  Germany    Europe  1982   73.800    78335266  22031.532740       DEU   \n",
              "571  Germany    Europe  1987   74.847    77718298  24639.185660       DEU   \n",
              "572  Germany    Europe  1992   76.070    80597764  26505.303170       DEU   \n",
              "573  Germany    Europe  1997   77.340    82011073  27788.884160       DEU   \n",
              "574  Germany    Europe  2002   78.670    82350671  30035.801980       DEU   \n",
              "575  Germany    Europe  2007   79.406    82400996  32170.374420       DEU   \n",
              "696    India      Asia  1952   37.373   372000000    546.565749       IND   \n",
              "697    India      Asia  1957   40.249   409000000    590.061996       IND   \n",
              "698    India      Asia  1962   43.605   454000000    658.347151       IND   \n",
              "699    India      Asia  1967   47.193   506000000    700.770611       IND   \n",
              "700    India      Asia  1972   50.651   567000000    724.032527       IND   \n",
              "701    India      Asia  1977   54.208   634000000    813.337323       IND   \n",
              "702    India      Asia  1982   56.596   708000000    855.723538       IND   \n",
              "703    India      Asia  1987   58.553   788000000    976.512676       IND   \n",
              "704    India      Asia  1992   60.223   872000000   1164.406809       IND   \n",
              "705    India      Asia  1997   61.765   959000000   1458.817442       IND   \n",
              "706    India      Asia  2002   62.879  1034172547   1746.769454       IND   \n",
              "707    India      Asia  2007   64.698  1110396331   2452.210407       IND   \n",
              "\n",
              "     iso_num  \n",
              "168       76  \n",
              "169       76  \n",
              "170       76  \n",
              "171       76  \n",
              "172       76  \n",
              "173       76  \n",
              "174       76  \n",
              "175       76  \n",
              "176       76  \n",
              "177       76  \n",
              "178       76  \n",
              "179       76  \n",
              "564      276  \n",
              "565      276  \n",
              "566      276  \n",
              "567      276  \n",
              "568      276  \n",
              "569      276  \n",
              "570      276  \n",
              "571      276  \n",
              "572      276  \n",
              "573      276  \n",
              "574      276  \n",
              "575      276  \n",
              "696      356  \n",
              "697      356  \n",
              "698      356  \n",
              "699      356  \n",
              "700      356  \n",
              "701      356  \n",
              "702      356  \n",
              "703      356  \n",
              "704      356  \n",
              "705      356  \n",
              "706      356  \n",
              "707      356  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_df = gap[gap['country'].isin(['India','Brazil','Germany'])]\n",
        "temp_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "yOy4Y_Mab4ql",
        "outputId": "5913a986-e636-4325-8a71-8a0750432021"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f68616088e0>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 614.5x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.relplot(kind='line', data=temp_df, x='year', y='lifeExp', hue='country', style='continent', size='continent')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "x_PsEsifb6x8",
        "outputId": "14bba9bb-921a-4c8f-f56d-78ef72277e87"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='year', ylabel='lifeExp'>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lineplot(data=temp_df, x='year', y='lifeExp', hue='country')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "E395Tubvb8hd",
        "outputId": "fc0f29be-505e-406b-d64a-e4e63d600dd3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f68630464d0>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# facet plot -> figure level function -> work with relplot\n",
        "# it will not work with scatterplot and lineplot\n",
        "sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter', col='sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmK0k8_1b_Fv"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}